The learning process to train a model using training data plays a pivotal role in machine learning. The trained model helps researchers predict test data with high precision. When the number of features dimensions are too high, the learning process takes a long time or the trained model will perform poorly due to the CoD. Due to mentioned obstacles, data analytics phases take longer time and do not match well with visual computing solutions. To do real-time processing, a large number of studies have been explored in this paper to provide a trend in the world of image processing. All the studies state, and it goes without saying, that when higher number of resources and processors are available, the chance of computing in real-time increases [1]}. While reviewing the above-mentioned studies, we found a direct relationship between the efficiency of the algorithms for data analytics on visual computing and the richness of systems' hardware.
